US5122976A - Method and apparatus for remotely controlling sensor processing algorithms to expert sensor diagnoses - Google Patents
Method and apparatus for remotely controlling sensor processing algorithms to expert sensor diagnoses Download PDFInfo
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- US5122976A US5122976A US07/491,926 US49192690A US5122976A US 5122976 A US5122976 A US 5122976A US 49192690 A US49192690 A US 49192690A US 5122976 A US5122976 A US 5122976A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
- G05B23/0229—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions knowledge based, e.g. expert systems; genetic algorithms
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0221—Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/10—Plc systems
- G05B2219/13—Plc programming
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/37—Measurements
- G05B2219/37325—Multisensor integration, fusion, redundant
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2223/00—Indexing scheme associated with group G05B23/00
- G05B2223/06—Remote monitoring
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S706/00—Data processing: artificial intelligence
- Y10S706/902—Application using ai with detail of the ai system
- Y10S706/903—Control
- Y10S706/906—Process plant
Definitions
- the present invention is a method of controlling remotely located calculation algorithms based on expert system diagnoses and, more particularly, allows the diagnosis of sensor failure by a remotely located expert system and the transfer of the results of the diagnosis to a sensor sampling and plant control system which controls sensor calculations and control calculation based on the diagnosis.
- Complex calculations are currently done by plant data centers located at complex plants such as electric power generating plants.
- An example a complex calculation is a thermodynamic turbine efficiency calculation. Such a calculation is based on on-line data and a bad sensor value can produce sharp results, such as turbine efficiencies greater than 100% or less than 0%. Since turbine efficiency is used by the plant operator to guide control operations attempting to maximize plant efficiency, bad calculation results can prevent the operator from obtaining maximum plant efficiency.
- complex sensor analysis algorithms are provided in the data center computer to cope with the many possible problems that sensors can experience. When a new sensor problem occurs, the complex sensor analysis algorithms frequently break down and the complex calculations based on uncorrected sensor values are performed. What is needed is a method of performing sensor diagnosis which relieves the plant data center computer from the task of sensor diagnosis and allows the plant data center computer to concentrate on on-line data acquisition and process control.
- the remotely located diagnostic centers continue to receive bad data in such situations, and, as a result, faulty high level system diagnoses are made or the diagnostic system simply sends a message to the plant operator indicating that the sensor must be fixed before certain high level diagnoses can be made.
- the diagnostic system simply sends a message to the plant operator indicating that the sensor must be fixed before certain high level diagnoses can be made.
- no communication is performed between the data collection routine and the expert diagnostic system. What is needed is a method allowing the sensor scan and processing routines to communicate with the diagnostic system.
- a system that uses a data center computer, used for data collection and process control of a complex processing plant, to collect raw sensor data which is sent to a remote expert system for sensor diagnosis.
- the diagnostic results are returned to the data center computer where the results are used to control sensor processing routines used to control combine sensor signals and to the plant based on the collected sensor data.
- FIG. 1 illustrates the operation and components of the present invention
- FIG. 2 illustrates an example of a rule for diagnosing faulty sensors
- FIG. 3 illustrates a matrix of diagnostic results
- FIG. 4 depicts the data structures used by the preferred expert system used in the present invention.
- FIG. 5 illustrates expert system diagnostic message processing
- FIG. 6 illustrates message processing by the plant data center computer
- FIGS. 7 and 8 depict alternative methods of performing sensor processing based on diagnostic results.
- a plant data center computer 10 such as a Digital Equipment Micro VAX computer, obtains sensor data from sensors 12 and uses that data to control a complex processing plant 14 such as a nuclear or fossil-fueled power plant.
- the present invention rather than have the data center computer 10 perform sensor analysis and continues processing when sensors are faulty, transmits the sensor data to a diagnostic operation center computer 16, such as a Digital Equipment VAX computer which diagnoses sensor problems using an expert system such as the PDS (Process Diagnostic System) available from Westinghouse and described in U.S. Pat. Nos. 4,644,479 and 4,649,515 incorporated by reference herein.
- Other expert systems could be used for the sensor diagnostic task as long as they have the capability of diagnosing sensor malfunctions.
- a conventional sensor scan routine 20 in the plant computer 10 scans the sensors 12 and loads the sensor data into a raw sensor data file 22.
- the collated sensor data is deadbanded.
- a conventional communication router routine or utility such as DEC-NET available on the Digital Equipment VAX VMS computer systems, formats the sensor data into a message and transmits the deadbanded sensor data over a conventional communication network such as a telephone line or a microwave data communication system to the operation center diagnostic computer 16.
- a corresponding communication routine 26 parses the message and stores the raw sensor data in a raw sensor data file 28.
- an expert system 30 is informed (as indicated by the dashed line) and the raw sensor data is accessed by the expert system inference engine 30, such as the PDS system previously mentioned, which uses rules in a rulebase 32 to diagnose faulty sensors.
- the results of the diagnosis are stored in a node list database 34 maintained internally to PDS. This list is accessible through a PDS utility.
- PDS also produces two output files, the first is a complete list of all malfunction nodes with a confidence above 0.0 and is used by people monitoring the diagnostic system, and the second is a list of all malfunction nodes with a confidence above 0.5 and is sent to the display 52 in the plant data center 10.
- Each diagnosis has associated therewith a diagnostic indicator or value which, as will be discussed in more detail later, is used by the data center computer 10 to control subsequent sensor processing.
- a message routine 36 accesses the node list database for sensor processing control messages and stores the messages in a message file 38.
- the communication routine 26 is activated to transfer the contents of the message file 38 to the communication routine 24 which creates a corresponding message file 40.
- a message interpreter routine 42 illustrated in more detail in FIG. 6, is started which reviews the messages and stores the sensor processing routine control values in appropriate locations in a calculation control data file 44.
- Sensor processing routines examples of which are illustrated in more detail in FIGS. 7 and 8, once the message interpreter routine 42 has completed execution, obtained the calculation control data from file 44 and process the raw sensor data into processed sensor data which is stored in an appropriate file 48 or perform actual process control operations on the plant 14.
- the routine 42 can wake up the routines 46, it is preferable that the routines 46 wake up based on a timed interrupt basis.
- the control value indicates a change in a parameter of the scan routine, such as changing the scan cycle from 10 seconds to 20 seconds in an attempt to reduce transient noise
- the sensor scan routine 20 can obtain the control value and make appropriate scan cycle adjustments. It is also possible for the deadband limits for the sensor can be adjusted.
- the communication routine 24 can then send the processed sensor data to the diagnostic computer 16 which can use the expert system previously discussed to perform high level diagnostic operations, such as for determining whether maintenance on a piece of equipment such as a generator should be performed.
- the results of such higher level diagnostic operations can be communicated back to the data center computer 10 where other processing 50 routines such as display processing routines can provide the results of the higher level diagnoses to a display 52 for review by an operator 54.
- the sensor data 22 as it is processed, based on the diagnosis can also be immediately used for control operations rather than first storing it as a file 48 and then performing control operations.
- a system such as that illustrated in FIG. 1 shifts the burden of sensor malfunction diagnosis to a type of software which is capable of more efficiently performing such diagnoses.
- This system also relieves the data center computer 10 of the burden of sensor diagnosis.
- Such a system also allows plants with smaller data center computers to take advantage of complex calculation systems which require accurate sensor data, such as plant efficiency calculation systems, without having to retrofit the plant data center computer with complex sensor analysis software.
- FIG. 2 illustrates the operation of a typical rule for producing a calculation control value associated with hot reheat pressure sensors.
- the rule illustrated in FIG. 2 has as inputs results of diagnoses associated with the hot reheat pressure sensor 70, the cold reheat pressure sensor 72 and the extraction pressure sensor 74.
- the diagnoses for the sensors themselves are within the skill of the ordinarily skilled expert system knowledge engineer and perform relatively simple tasks such as testing the raw sensor data against valid data range limits.
- FIG. 4 illustrates the hypothesis node 90 slots, malfunction node 92 slot and the message node 94 slots as would be used by the preferred expert system when producing the diagnoses stored in the node list data base 34.
- the node list database is filled with message slots as illustrated by the block 94 in FIG. 4.
- the message list database is scanned by the message routine 36 until a message node slot is encountered and the contents of this message node slot are fetched 102, as illustrated in FIG. 5. If the confidence factor falls 104 between the maximum and minimum confidence factors, a test 106 is performed on the value field of the message node slot to determine whether the value field includes a node name.
- a test 106 is performed on the value field of the message node slot to determine whether the value field includes a node name.
- indirect references to calculation control values for the sensor processing routines can be provided. This allows many different diagnostic rules to refer to the same diagnostic control value making changes to the control values easy. This also allows other rules to calculate control values, such as a sensor average and place them in an indirectly accessed variable node. If the value is a node name the value is fetched 108 from the named node and then a file entry, which includes the message name, the destination and the value, is stored 110 in message file 38. If no additional messages are encountered 112, the routine stops.
- the message interpreter routine 42 stores the sensor processing routine control values in the appropriate locations of the calculation data control file 44.
- This routine first decodes 122 the message to separate the message name, destination and control value.
- the destination is used in an index file, called a section file in VMS, to find 124 the actual location of the place where the message value should be stored and then the message value is stored 126 in that destination location. If no more messages exist 128, the system stops.
- the computer 10 Prior to executing the actual calculations of a sensor processing routine the computer 10 first obtains 142 the appropriate control value from the destination location. This control value is tested 144 and 146 looking for a match to previously identified control values which correspond to different sensor data routines 148 and 150. If a match occurs the corresponding sensor data routine is executed. For example, if the sensor calculations normally use the average of three sensors, the average of two sensors can be computed when a sensor is bad and the control value indicates a two sensor average. Another possibility is for the control value to indicate sensor weights in a weighted average or even that a virtual sensor value should be calculated from different sensors. If a match does not occur, a default sensor data routine 152 is performed.
- the present invention could execute the appropriate routines through the function capability available in a language such as FORTRAN.
- the function type execution routine as illustrated in FIG. 8, would first get 162 the appropriate control value at the destination location and store 164 this value in the appropriate field of a function statement. The function statement would then be executed 166. It is also possible for the sensor processing routine to load a predetermined or calculated value into a common data storage area.
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- Bioinformatics & Computational Biology (AREA)
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Abstract
Description
Claims (10)
Priority Applications (7)
Application Number | Priority Date | Filing Date | Title |
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US07/491,926 US5122976A (en) | 1990-03-12 | 1990-03-12 | Method and apparatus for remotely controlling sensor processing algorithms to expert sensor diagnoses |
ITMI910416A IT1252311B (en) | 1990-03-12 | 1991-02-19 | APPARATUS TO CONTROL SENSOR SIGNAL PROCESSING ALGORITHMS IN RESPONSE TO DIAGNOSIS OF AN EXPERT SYSTEM |
ES9100623A ES2029420A6 (en) | 1990-03-12 | 1991-03-11 | Method and apparatus for remotely controlling sensor processing algorithms to expert sensor diagnoses |
KR1019910003850A KR910017308A (en) | 1990-03-12 | 1991-03-11 | Devices that control sensor processing algorithms in response to expert sensor diagnostics |
CN91101471A CN1026832C (en) | 1990-03-12 | 1991-03-11 | Apparatus for controlling sensor processing algorithms response to expert sensor diagnoses |
CA002037948A CA2037948A1 (en) | 1990-03-12 | 1991-03-11 | Method and apparatus for remotely controlling sensor processing algorithms responsive to expert sensor diagnoses |
JP3046552A JPH0765911B2 (en) | 1990-03-12 | 1991-03-12 | Sensor data calculation controller |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
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US07/491,926 US5122976A (en) | 1990-03-12 | 1990-03-12 | Method and apparatus for remotely controlling sensor processing algorithms to expert sensor diagnoses |
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US5122976A true US5122976A (en) | 1992-06-16 |
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US07/491,926 Expired - Fee Related US5122976A (en) | 1990-03-12 | 1990-03-12 | Method and apparatus for remotely controlling sensor processing algorithms to expert sensor diagnoses |
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US (1) | US5122976A (en) |
JP (1) | JPH0765911B2 (en) |
KR (1) | KR910017308A (en) |
CN (1) | CN1026832C (en) |
CA (1) | CA2037948A1 (en) |
ES (1) | ES2029420A6 (en) |
IT (1) | IT1252311B (en) |
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Also Published As
Publication number | Publication date |
---|---|
ES2029420A6 (en) | 1992-08-01 |
KR910017308A (en) | 1991-11-05 |
ITMI910416A1 (en) | 1992-08-19 |
IT1252311B (en) | 1995-06-08 |
ITMI910416A0 (en) | 1991-02-19 |
CA2037948A1 (en) | 1991-09-13 |
CN1026832C (en) | 1994-11-30 |
CN1054842A (en) | 1991-09-25 |
JPH0765911B2 (en) | 1995-07-19 |
JPH04220521A (en) | 1992-08-11 |
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